Data Science Projects for Freelancers

Looking for an experienced AI engineer or solutions architect to provide hands-on technical mentorship focused on building reliable, production-ready AI systems.

Key topics:

* Advanced RAG, document extraction, and multimodal processing
* Knowledge graphs, GraphRAG, and contextual retrieval
* AI agents, multi-agent orchestration, and automation
* Cloud infrastructure, deployment, and cost optimization
* LLM evaluation, testing, and system reliability

What I’m looking for:

Someone with real production experience who can explain architectural trade-offs, review implementations, and guide practical exercises.

Sessions should be interactive, project-based, and focused on engineering decisions rather than introductory theory.

Please share your relevant experience, mentoring approach, hourly rate, and availability.

We are seeking an experienced AI & Data Architecture Consultant to help design and implement scalable data infrastructure for our AI-powered platform.

The ideal candidate has strong experience in data architecture, cloud infrastructure, and modern AI technologies, including LLMs, RAG, vector databases, and AI agents.

**Key Responsibilities:**
- Design database schemas, data models, and scalable architectures.
- Build AI data pipelines and RAG workflows.
- Integrate LLMs, vector databases, and APIs.
- Recommend AWS infrastructure and storage solutions.
- Optimize system performance, security, and costs.
- Provide technical consulting and architecture documentation.

**Requirements:**
- 5+ years in data engineering or architecture.
- Strong Python, SQL, PostgreSQL, and AWS experience.
- Familiarity with LangChain, LangGraph, and AI agents.
- Strong communication and problem-solving skills.

**Engagement:** We will start with 2–3 small paid consulting tasks to evaluate technical expertise and collaboration. Successful completion may lead to a long-term, part-time engagement.

**Rate:** $50–$70/hour | Remote | Flexible schedule

I want to turn publicly available well information into a genuinely original petroleum-engineering or geology study. Your task starts with a focused literature review that pinpoints a clear gap in current knowledge, then uses real data from existing wells to fill that gap.

What matters most to me is ending up with a complete study plan that already contains the data, the analysis, and thorough documentation. I am flexible about the discipline focus—reservoir behaviour, log interpretation, drilling performance, or any other angle—provided the idea is fresh, technically sound, and demonstrably feasible.

Here is the flow I expect:
• Locate and reference suitable open-source well datasets.
• Formulate specific, testable research objectives tied to the identified gap.
• Lay out a practical methodology, noting any models, coding languages, or software you choose.
• Carry out enough analysis to show the approach works, interpret the results, and explain the limitations.
• Package everything—datasets (or download links), cleaned data files, calculation sheets or scripts, result visuals, and a narrative report—so another researcher can replicate the work without guesswork.

Keep the writing concise, cite sources accurately, and make the analytical steps transparent. I will be available for quick feedback as the project moves from idea selection to final documentation.

Skills: Research, Geology, Petroleum Engineering, Statistical Analysis, SPSS Statistics, Data Science, Data Analysis, Data Management

I need an expert to create a detailed GIS map and conduct an environmental risk assessment of licensed gambling premises in a specific local authority area.

Key Tasks:
- Map licensed gambling premises within the designated area.
- Measure gambling exposure by analyzing both the distance to the nearest licensed premises and the number of licensed premises within each neighbourhood.
- Compare the number of gambling premises with:
- Deprivation levels
- Population age and household characteristics
- Health and social vulnerability indicators

Ideal Skills and Experience:
- Proficiency in GIS mapping software (e.g., ArcGIS, QGIS)
- Strong analytical skills
- Experience in environmental risk assessment
- Knowledge of social demographics and public health data

Please provide samples of previous work and a brief outline of your approach.

Skills: Cartography & Maps, Geospatial, Statistical Analysis, Data Science, Geographical Information System (GIS), Data Analysis, Risk Assessment, ArcGIS

We are looking for a detail-oriented and tech-savvy Data Analyst to join our team. In this role, you will help us transform raw data into actionable insights by building dashboards, managing spreadsheets, and leveraging the latest AI tools to optimize your workflow.

Candidates should have advanced knowledge of spreadsheet functions including LAMBDA, Named Function, Query, Let, Regex and be able to handle complex and vast datasets. Familiarity with Google App Script a plus. Candidates will need to become familiar with our data structure and learn how to find the right type of answers.

Additionally, candidates will assist teams with various admin duties both routine and ad hoc. Should be able to work in a structured environment, be adaptable to change, attend daily huddles and weekly meetings. This is a fully remote position.

If you are a spreadsheet wizard, a Power BI expert, and someone who loves using AI to build tools and work smarter, we want to hear from you!

Key Responsibilities

- Daily updating of scorecards, dashboards: Pull reports and update scorecards. Sharing updated reports with stakeholders.
- Data Processing & Management: Clean, organize, and maintain datasets across Google Sheets. Writing and maintaining advanced Google Sheets formulas derived from business rules to align with organizational standards.
- Dashboard Creation: Design, build, and maintain interactive reports and dashboards using Power BI.
- AI Integration: Utilize AI technologies, specifically Claude, ChatGPT/CodeX and other platforms, to accelerate data analysis, generate insights, format data, troubleshoot complex formulas, and create automated workflows.
- Reporting: Translate complex data sets into clear, easy-to-understand visual reports for stakeholders.

Required Skills & Qualifications

- Spreadsheet Mastery: Advanced proficiency in Microsoft Excel and Google Sheets (complex formulas, pivot tables, data modeling, etc.).
- Data Visualization: Strong, proven experience building interactive dashboards, Data Modeling, and Writing DAX in Power BI.
- AI Curiosity & Familiarity: A strong interest or foundational experience using AI language models (such as Claude or ChatGPT) to assist with data manipulation, analysis, or scripting.
- Analytical Thinking: Strong problem-solving skills and the ability to draw meaningful conclusions from raw data.

Bonus Points (Preferred but NOT Required)

- Familiar with the Google Workspace ecosystem.
- Practical expertise with end-to-end data pipelines and data warehousing architecture.
- Working knowledge of databases and querying languages (e.g., Supabase, SQL, etc.).
- Familiarity with other data manipulation or visualization tools (e.g., Python).
- Experience with automating tasks using Google Apps Script or VBA.

I have 10 yrs experience in data science.
Now want to move to gen ai architect role.
Have theoretical knowledge for few concepts like agentic Langgraph, rag, MCP, familiarity with AWS etc
Need someone to make me job ready for this role and should be able to implement things when given at work.

Currently not doing job, do flexible with timings and need things at fast pace.

So, looking for good ai architect trainer.
Budget is fixed, so if this budget is not aligned, please don't apply.

# Machine Learning Tutor Needed – Arabic Speaker

### Job Description

We are looking for an experienced **Machine Learning Tutor who is fluent in Arabic** to provide online tutoring and academic support for university-level Machine Learning topics.

The ideal candidate should have strong theoretical and practical knowledge of Machine Learning, with the ability to explain complex concepts clearly and simply in **Arabic**, while using English technical terminology when necessary.

### Topics to Be Covered

1. **Introduction to Machine Learning**
- Machine Learning concepts and applications
- Supervised and unsupervised learning
- Machine Learning workflow

2. **Data Preprocessing**
- Data cleaning and preparation
- Handling missing values and outliers
- Feature selection and feature engineering
- Training and testing datasets

3. **Supervised Learning**
- Linear and logistic regression
- Decision trees and random forests
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)

4. **Unsupervised Learning**
- K-Means clustering
- Hierarchical clustering
- Principal Component Analysis (PCA)

5. **Model Evaluation and Optimization**
- Accuracy, precision, recall, and F1-score
- Confusion matrix
- Cross-validation
- Overfitting and underfitting
- Hyperparameter tuning

6. **Practical Machine Learning Using Python**
- Python fundamentals for Machine Learning
- Pandas, NumPy, Matplotlib, and Scikit-learn
- Building, training, and evaluating ML models
- Hands-on exercises using real datasets

### Requirements

- **Fluent Arabic speaker (mandatory).**
- Strong background in Machine Learning, Artificial Intelligence, or Data Science.
- Experience teaching or tutoring university students.
- Excellent knowledge of Python and Machine Learning libraries.
- Ability to explain mathematical concepts and algorithms step by step.
- Experience providing practical demonstrations and exercises.
- Available for interactive online sessions via Zoom.

### Responsibilities

- Deliver structured, one-to-one online tutoring sessions.
- Explain theoretical concepts and practical applications in Arabic.
- Provide hands-on Python examples and exercises.
- Help students understand lecture materials and solve practice problems.
- Support preparation for quizzes, exams, and Machine Learning projects.

### How to Apply

Interested freelancers should provide:

- A brief introduction and relevant qualifications.
- Previous experience teaching Machine Learning.
- Topics and programming tools they specialize in.
- Hourly tutoring rate and availability.
- Confirmation of Arabic fluency.

**Preference will be given to tutors who can simplify complex Machine Learning concepts and provide practical, step-by-step explanations in Arabic.**

We are a B2B data intelligence platform. We sell signals to commercial teams: which companies are hiring, spending, moving people and warming up as buyers. Customers include large media and agency groups. They come to us for one thing: fresh data they can export.

Every dataset in the app is judged on three numbers: coverage, accuracy and recency. This role exists to push all three up, one dataset at a time.

You will run whole epics on your own. That means working out what customers need, finding where the data lives, building or buying the pipeline, landing it in BigQuery, building models on top of it, checking it with LLMs and shipping it to production down to the column and cell. Then you keep it fresh. You will not be handed a spec. You will write it.

We are AI-native. Claude Code, connected to our warehouse over MCP, is how we explore data, write dbt models and pipelines, and QA output. We run our own MCP servers. If you still do by hand what an agent could do, this isn't the role for you.

THE LOOP YOU ARE BUILDING
A customer asks for something we don't have, or have only thinly. You size the demand and decide whether it's worth building. You find the precursor data, whether that's public sources, vendors, APIs or our own engines. You scrape it or license it and land it in BigQuery. You transform it in dbt and build data science models on top of it, such as entity matching, classification and enrichment, to a very high accuracy bar. You QA every model with LLM checks against ground truth. You deploy the columns and cells that pass, through ClickHouse and Elasticsearch, into the app. You set a refresh schedule that costs as little as possible for the freshness the customer needs. Coverage, accuracy and recency go up, and you report all three.

That loop is the job. A dataset that's 90% accurate but six months stale is worth less than one that's 85% accurate and refreshed weekly. You will be the one deciding where that trade-off sits.

WHAT YOU BUILD: FOUR LAYERS
1. Demand → dataset. Turn customer requests, support tickets, product usage in PostHog and sales calls into a ranked backlog of data models to build. You write the case for each one: who wants it, what it unlocks and what it costs to keep fresh.
2. Sourcing: build or buy. Custom scrapers for public sources, written within each site's terms and with privacy in mind. They're built with Playwright, Apify or Bright Data and run on GCP. Vendor evaluation where buying beats building: sample pulls, match rates against our entity base, cost per useful row and refresh SLAs. You bring us a recommendation with numbers, not a list of vendors.
3. Modelling and LLM-driven QA. dbt models on BigQuery, from raw data through staging to the marts the app reads. You build the data science models on top: matching, deduplication, classification and enrichment. Automated QA uses LLMs to check, reconcile and score records before they reach a customer. You prototype in Claude Code against the warehouse and prove the model there. Only then does it go to production.
4. Storage, refresh and deployment. You decide where each dataset lives and how it reaches the app: BigQuery, ClickHouse, Elasticsearch, Postgres and Redis. You also decide what to fetch at request time and how often to re-pull, through dbt Cloud jobs, Dagster, Pub/Sub and Cloud Run. Deployment happens at column and cell level, so a weak field never drags a strong dataset down with it. Lightweight endpoints and internal tools ship on Vercel, and failures alert through Sentry and Datadog.

WHAT YOU OWN END TO END
- The data backlog, ranked by customer demand and cost to serve
- Scrapers, vendor integrations and the pipelines that join them
- dbt models and the data science models built on top of them
- Vendor evaluations and build-vs-buy recommendations
- LLM QA processes and the accuracy benchmarks behind them, running continuously rather than once
- Storage and refresh architecture, with GCP cost per dataset
- The weekly coverage, accuracy and recency report, by dataset and by field

30 / 60 / 90
- Day 30: Current datasets audited for coverage, accuracy and recency. First epic picked and scoped. LLM QA running on a schedule against at least one existing dataset.
- Day 60: First new dataset sourced, modelled in dbt, QA'd and deployed to the app at column level. First vendor evaluation delivered with a recommendation. Refresh and storage cost per dataset measured.
- Day 90: Two epics shipped end to end, each on a refresh schedule with ongoing QA. Measurable gains in coverage, accuracy or recency on the datasets you touched. A demand-ranked backlog for the next quarter.

SUCCESS METRICS
- Coverage, accuracy and recency, by dataset. This is the north star. Every other metric feeds it.
- Model accuracy against ground truth. It stays high after launch, not just on launch day.
- Cost per fresh record. It goes down as you optimise storage and refresh across GCP.
- QA pass rate before production. Nothing ships to a customer without passing.
- Epics shipped end to end. Shipped means live in the app, not stuck in a notebook.

COMPENSATION
Full time, 40 hours a week, $24–42/hr depending on experience. Tooling, vendor sample and compute costs are paid separately from your fee.

REQUIREMENTS
- You have done the full loop yourself, more than once. You sourced raw data, pulled it into a database, built data science models on it to a very high accuracy, deployed those models into a production app, then maintained and refreshed them with ongoing QA. This is the most important requirement. Your GitHub or Loom should show it.
- AI-native. You use Claude Code every day, including with MCP connected to a data warehouse, to write code, build pipelines and QA data.
- dbt on BigQuery in production. You have worked with incremental models, tests, sources and the cost of a careless full refresh.
- ClickHouse. You know when to serve from ClickHouse rather than query BigQuery, and the cost and latency trade-offs between them.
- Orchestration. You have used dbt Cloud plus Dagster or a similar orchestrator, and you think in assets, schedules, sensors and freshness.
- GCP. You are comfortable with Cloud Run, Pub/Sub, Cloud Storage, IAM and keeping a GCP bill under control.
- Production scrapers you built yourself with Playwright, Apify, Bright Data or similar, including rate limiting, anti-bot handling, monitoring and fixes when they break.
- Strong Python and SQL. You are at home with entity matching, deduplication and schema design.
- LLMs for real data work through the Anthropic or OpenAI APIs, such as extraction, classification and validation, and you can show how you measured accuracy.
- Vendor evaluation. You have evaluated or integrated third-party data vendors and can explain match rate, fill rate and freshness without looking them up.
- Autonomy. You can take a vague goal, turn it into a plan and ship it without daily check-ins.
- GDPR awareness. You work with UK and EU data, and scraping and personal data both sit inside privacy review.
- Clear written English. You will write the specs, vendor memos and weekly reports.

Nice to have: Elasticsearch. Postgres and Redis. Building MCP servers in Python. TypeScript, GraphQL (Apollo Federation) or Next.js, so you can follow your data into the app. Vercel and Supabase. Terraform, GKE and Cloud Build. Clay and Make. Product management experience with data products.

Our stack: Claude Code + MCP, Anthropic and OpenAI APIs, Python, SQL, dbt (dbt Cloud), BigQuery, ClickHouse, Elasticsearch, Postgres, Redis, Dagster, GCP (Cloud Run, Pub/Sub, GKE, Cloud Build), Terraform, Playwright, Apify, Bright Data, GraphQL, Vercel, Supabase, PostHog, Sentry, Datadog, Make, Clay, Linear and Slack.

How we work: Async-first, any timezone. You need to be reachable on Slack and you need to ship. Hacky and shipped over polished and planned. If the first version of a new dataset is a scraper, a Claude QA prompt and one table of cells that passed, that is correct. Make it work, show us the numbers, then automate it.

HOW TO APPLY: READ THIS CAREFULLY
Proposals without one of these will not be read:
- A GitHub link to a scraper, pipeline or data model you built yourself, or
- A Loom of 5 minutes or less walking through one dataset you took through the full loop: where you sourced it, how you stored it, the model you built on it and its accuracy, how it got into production and how you kept it fresh and QA'd.

Start your proposal with the word "Recency" so we know you read this far. Proposals that look like templates or are written entirely by AI will be rejected.

***What We're Looking For***

An experienced data architect, principal analytics engineer, or former Head of Data who combines deep technical expertise with strong strategic judgment, experience leveraging AI, and an interest in coaching others.

Key capabilities include:

· Data architecture and engineering, particularly Google Cloud, BigQuery, SQL, and Power BI.

· Data pipelines, transformation frameworks, semantic modeling, testing, and automation.

· Scalable architecture, data governance, version control, and development standards.

· AI-assisted programming and analytics, including Claude Code or similar tools, with particular emphasis on secure implementation.

· Natural-language querying, AI-enabled reporting, and self-service analytics.

· Technology stack evaluation, technical roadmapping, and prioritization.

· Technical mentorship, team development, and leadership assessment.

Healthcare experience and familiarity with financial and operational analytics are beneficial but not required.


***About Us***

Armina Health is a multi-entity healthcare organization operating across multiple specialties and states. Our internal data and analytics platform integrates data from multiple EMR and operational systems.

Platform: Google Cloud, BigQuery, SQL, and Power BI

Where we are: We’ve made considerable progress building automated financial and operational reporting.

What we need: An experienced technical leader to evaluate our existing architecture, guide our long-term technology and AI strategy, and accelerate the development of our internal analytics team.

What we’re not after: Someone to take over development indefinitely. Our goal is a more reliable, scalable infrastructure and a technically capable, independent team that can increasingly operate without outside support.


***Objectives***

1. Evaluate Our Architecture and Technical Direction

Assess our existing data infrastructure, reliability, scalability, documentation, and development practices.

Identify material risks, opportunities for improvement, and recommended changes to our technology stack.

Help establish a practical, prioritized technical roadmap that supports our future growth without introducing unnecessary cost or complexity.

We want someone who can challenge our assumptions and help us understand what we may be overlooking, rather than simply improving the tools we're already using.

2. Develop Our AI Strategy and Self-Service Analytics Capabilities

We have a fairly traditional, structured analytics environment today. Data is consolidated and transformed in Google BigQuery, and recurring reports and dashboards are automated through Power BI.

We want to understand what more is possible.

In particular:

· How can we leverage AI to make reporting and analysis substantially faster, easier, and more accessible?

· Can we enable executives to ask questions of our data in natural language and generate their own initial analyses, reports, or visualizations?

· Can we create a workflow where executives perform preliminary analysis independently, then engage our analysts to validate findings, investigate further, or develop more sophisticated reporting?

· How should our data architecture, semantic models, documentation, and governance evolve to support reliable AI-generated analysis?

· How can Claude Code, smaller specialized models, or other AI tools accelerate programming, data engineering, reporting, and automation?

· What infrastructure, security controls, model architecture, and data-access restrictions are necessary to implement these capabilities safely, particularly with sensitive healthcare information?

We're interested in practical solutions that build on our existing investments, not necessarily replacing our current platform.

Our desired outcome is to move beyond a model where executives request reports and wait for analysts to produce them, toward one where leaders can explore trusted data independently, with analysts increasingly focused on validation, deeper analysis, and higher-value work.

3. Develop a More Capable and Independent Team

Provide technical coaching, architectural guidance, and development support to our existing analytics team.

Initially, we envision participation in our weekly coordination meetings, review of current technical challenges, guidance on development approaches, and direct coaching as needed.

We want to improve the team's technical judgment, programming capabilities, engineering practices, and ability to evaluate new technologies.

The objective is greater independence, better decision-making, and less reliance on any single technical resource or executive oversight.

4. Evaluate Our Future Leadership and Organizational Needs

Assess the capabilities, strengths, and development opportunities of our existing analytics team.

Help us determine:

· What technical, strategic, and managerial capabilities our future Director of Data & Analytics should possess.

· Whether someone on our current team has the potential to grow into that position.

· What development or recruiting would be needed to close existing gaps.

· Whether a full-time director is necessary or whether a stronger internal team supported by fractional technical leadership could be a better structure.

We want to define the right leadership solution based on the actual needs of the organization.


***Engagement Structure***

We envision a longer-term relationship with a decreasing level of involvement as our team and infrastructure mature.

Phase 1 — Initial Assessment & Technical Integration (Approximately 4–8 Weeks)

Estimated commitment: 10–20 hours per week

· Evaluate the existing architecture, technology stack, AI readiness, and team capabilities.

· Begin attending weekly team coordination meetings and providing technical guidance immediately.

· Identify risks, capability gaps, and opportunities for improvement.

· Recommend an initial AI and self-service analytics strategy.

· Develop a prioritized technical roadmap.

· Recommend future team structure and leadership requirements.

We invite candidates to propose an appropriate assessment scope, timeline, specific deliverables, and fixed fee. The assessment should be practical and proportionate to our environment rather than an unnecessarily extensive consulting engagement.


Phase 2 — Roadmap Execution & Team Development (Approximately Months 3–6)

Estimated commitment: 8–15 hours per week

· Help the team execute the highest-priority roadmap initiatives.

· Provide technical coaching, architecture review, and ongoing guidance.

· Support pilot implementation of AI-assisted development and self-service analytics.

· Establish and reinforce development standards, documentation, testing, and governance.

· Continue developing internal capabilities and potential future technical leadership.

· Reassess priorities and technical needs as we make progress.

Our internal team will remain responsible for implementation. The advisor will provide technical direction, coaching, and review, with occasional hands-on contributions by mutual agreement.


Phase 3 — Ongoing Fractional Advisory (After Approximately Six Months)

Estimated commitment: 3-6 hours weekly

· Provide continued architectural oversight and technical guidance.

· Participate in periodic team coordination and strategic planning.

· Advise on major technical decisions, new technologies, and AI opportunities.

· Coach team members as needed.

· Help ensure our architecture and capabilities continue to evolve with the business.

Occasional project work may be available and would be scoped separately.


***Division of Responsibilities***

· Advisor: Technical strategy, architecture evaluation, coaching, quality review, and recommendations.

· Internal Team: Development, implementation, maintenance, and documentation.

· Executive Leadership: Business priorities, resource allocation, and investment decisions.

We want someone who can help us build the function, not become indispensable to its daily operations.


***Ideal Candidate***

We're particularly interested in someone who has built or led a data and analytics function, has practical experience incorporating AI into modern data environments, and enjoys developing technical teams.

We value independent judgment, clear communication, curiosity, and an ability to balance sophisticated technical thinking with practical, cost-conscious solutions.


***How to Apply***

Please fill out the following form:

https://forms.gle/oZ3hDHGSTaesWAq29

Fractional Technical Engagement Lead
Remote | U.S. & Canada Preferred | International Applicants Welcome

Part-Time | Flexible Schedule | Approximately 5–10 Hours per Week
Job Summary
Bright North Peak is seeking accomplished professionals from engineering, science, research, technology, and specialized industry backgrounds to support our international expansion, particularly in North America.

This flexible, long-term fractional opportunity is designed for professionals who want to apply their expertise, industry knowledge, and professional experience to meaningful client engagements while collaborating with an experienced engineering team.
About the Company
Bright North Peak is a remote-first, globally distributed engineering company that helps organizations solve complex business and technology challenges through software engineering, artificial intelligence, cloud infrastructure, data platforms, automation, and modern system architecture.

As we expand our global presence, with a particular focus on the North American market, we're looking to build long-term relationships with accomplished professionals across diverse disciplines.

Our philosophy is simple: Trust creates opportunities. Engineering creates value. Long-term partnerships create lasting success.
About the Role
As a Fractional Technical Engagement Lead, you'll serve as a trusted professional counterpart, bringing your expertise, experience, and perspective to conversations with prospective clients and our engineering team.

Your primary contribution will be helping us understand real-world business, scientific, and industry challenges, establish professional credibility, and identify opportunities where our engineering capabilities can create meaningful value.

You'll collaborate with our engineering team as opportunities develop, contributing professional insight and judgment relevant to your area of expertise.

The role is structured around occasional discussions, opportunity evaluations, and ongoing professional collaboration, with flexible commitments that accommodate your existing career and responsibilities.
What You'll Do
- Build professional trust: Help establish credibility with prospective clients through your expertise, professional experience, and sound judgment.
- Understand client challenges: Participate in relevant discussions to understand business, scientific, technical, or industry-specific needs.
- Contribute domain expertise: Share practical insights, professional perspectives, and industry knowledge to help evaluate potential opportunities.
- Connect needs with capabilities: Collaborate with our engineering team to identify how technology and engineering solutions can address real-world challenges.
- Support client engagement: Join occasional client conversations and opportunity discussions where your expertise adds value.
- Strengthen long-term relationships: Contribute to professional relationships built on trust, clear communication, and successful engineering delivery.
Required Qualifications
- Bachelor's degree or higher in a relevant scientific, technical, engineering, or industry-related discipline.
- Genuine professional expertise in at least one relevant scientific, technical, engineering, research, or specialized industry field.
- A proven track record of meaningful professional achievements demonstrated through real-world projects, research, products, systems, or other verifiable accomplishments.
- Relevant professional, practical, or research experience in your area of expertise.
- Strong professional credibility, integrity, and communication skills.
- Excellent professional English communication skills.
- Ability to collaborate effectively with a globally distributed engineering team.
- Legal authorization to provide independent-contractor services from your country of residence.
Professional Backgrounds We Welcome
We welcome professionals with expertise in one or more of the following areas:

Engineering & Technology

Current or former Chief Technology Officers (CTOs), technology executives, principal engineers, software engineering, artificial intelligence, machine learning, data science, cloud computing, systems architecture, infrastructure, telecommunications, automation, and related disciplines.

Science & Research

Physics, mathematics, chemistry, biochemistry, biology, biotechnology, biomedical engineering, computational science, and other scientific or research disciplines.

Industry & Domain Expertise

Healthcare, life sciences, manufacturing, finance, energy, aerospace, automotive, logistics, telecommunications, and other specialized industries.

These are examples rather than an exhaustive list. Expertise in one relevant field is sufficient; candidates are not expected to have experience across multiple disciplines.
Work Arrangement
- Type: Fractional, independent contractor
- Location: Fully remote
- Preferred Locations: United States and Canada
- International Applicants: Qualified candidates from other countries are welcome
- Commitment: Approximately 5–10 hours per week
- Schedule: Flexible, coordinated around occasional discussions and collaboration
- Duration: Long-term opportunity
Compensation
We offer competitive, flexible compensation based on professional expertise, proven achievements, responsibilities, and overall contribution.

Specific compensation arrangements will be discussed individually during the selection process, with opportunities for long-term growth.
How to Apply
We welcome the following optional information:
- Links to publications, patents, GitHub, GitLab, Bitbucket, research, portfolios, professional certifications, conference presentations, successful projects, or other meaningful achievements.
- A brief description of the scientific, technical, or industry topics you follow most closely, including professional journals, research papers, or technical publications you regularly read.
We look forward to learning more about your expertise, professional achievements, and perspectives on the future of technology, science, and industry.

About Akerio
Akerio is a UK-based applied AI company. We build platforms that turn messy enterprise data into scored, auditable recommendations: the next diesel price for one of Europe's largest independent fuel distributors, a risk flag on a tenant across 41 shopping centres, a credit score a bank's committee can trace back to the source record. Our models are built by a PhD team from Stanford and the University of Chicago, every output shows what drove it and how confident it is, and the platform runs on-premise or in the cloud. We are live in production across energy, commercial real estate and financial services, with 18 AI modules running in one deployment alone. We are a small team shipping to demanding enterprise clients, so the work you do reaches production and gets used.

• Job: Full time
• Job type: Remote

The role
Our retail property analytics platform, live across 41 shopping centres, finished its first delivery phase and is moving into the next: new forecasting and cash models, a retail tenant version, and a product other mall operators can buy as a subscription. Every week, client feedback, AI assistant ratings and engineering findings land in the same Jira backlog. Someone has to decide what matters, write it so an engineer can build it without guessing, and prove to the client that it was delivered. That is you. You report to our CPO and work daily with engineering, data science, design and the client's asset, leasing and finance teams.

What you will work on
• Backlog ownership: turn client feedback, usage data and AI conversation reviews into prioritised Jira stories with clear acceptance criteria.
• Discovery with users: sit with leasing, finance and operations teams, map how they make a decision today, and find where a widget, forecast or AI answer would change it.
• Specs: short written specs for each feature: the problem, the user, the data it needs, edge cases, and what done looks like. Engineers use them with AI coding tools, so precision matters.
• Delivery tracking: plan sprints, track phase milestones, and prepare the input for monthly steering committee decks: what moved, what is at risk, what decision is needed.
• Acceptance: run user acceptance with the client, map deliverables to agreed scope, and close findings.
• AI product quality: review assistant conversations and ratings, group them into needs, and turn them into fixes to prompts, tools or widgets.
• Productisation: help shape the version of the platform that a second and third mall operator can adopt without custom work: onboarding, configuration and pricing tiers.

Tools you will use
Area Tools
Delivery Jira, Confluence, Figma
Product insight Langfuse (AI conversation and quality analytics), product usage data, SQL for your own questions
Communication Microsoft 365, Teams, PowerPoint for steering decks
Working with engineering Written specs, Bitbucket pull request reviews for acceptance, AI tools (Claude) for drafting and analysis
You do not write production code, but you should be able to read an API response, run a SQL query, and follow a technical discussion about NestJS services or an Airflow forecast run.

Your first six months
• First month: you know the platform module by module, have met the key client users, and own the backlog for one module.
• Month three: you own the full backlog and sprint planning, and the steering committee input comes from you.
• Month six: you lead discovery and scope for a new module or a new client rollout, from first workshop to signed-off acceptance.

What you bring
• 3 to 5 years as a product owner, product manager or business analyst on a B2B software product, ideally data or analytics heavy.
• You have written user stories and acceptance criteria that engineers built from without coming back to ask what you meant.
• Comfort with numbers: you can read a P&L line, question a forecast, and spot a wrong total on a dashboard.
• You have run sessions with client stakeholders, including senior ones, and managed scope when they asked for more.
• Basic SQL and comfort with Excel at a serious level.
• Fluent English and Turkish: current client teams work in Turkish, documentation and the wider company in English.

Nice to have
• Domain knowledge in commercial real estate, shopping centres, retail or finance.
• Experience with AI or LLM products: prompt design, evaluation, reading conversation logs.
• Agile certification (CSPO, PSPO) or a background in consulting or delivery management.
• Experience turning a custom client project into a repeatable SaaS product.

How we work
Small team, direct access to clients, and decisions in writing. We review the backlog every week, show progress to the client every month, and expect product people to argue for the user with evidence.

Hiring process
16. 30-minute intro call.
17. 30-minute session: present your case to the CPO and an engineer.
18. 15-minute call with a founder.
What we offer
• Fully remote, with overlap on UK and Türkiye working hours.
• Ownership of a live AI product with real users and a clear path to more client

About Akerio. Akerio is a UK-based applied AI company. We build platforms that turn messy enterprise data into scored, auditable recommendations: the next diesel price for one of Europe's largest independent fuel distributors, a risk flag on a tenant across 41 shopping centres, a credit score a bank's committee can trace back to the source record. Our models are built by a PhD team from Stanford and the University of Chicago, every output shows what drove it and how confident it is, and the platform runs on-premise or in the cloud. We are live in production across energy, commercial real estate and financial services, with 18 AI modules running in one deployment alone. We are a small team shipping to demanding enterprise clients, so the work you do reaches production and gets used.
• Job: Full time
• Job type: Remote

The role
Our clients do not buy a model. They buy a better decision: which lease to renegotiate this quarter, which tenant is about to pay late, how much footfall a mall will see next month. On our retail property platform, revenue, footfall, tenant risk and mall performance models already run every month through Airflow across 41 shopping centres. The next phase adds collections and cash projections, campaign ROI, and models trained per client. You own that model portfolio: accuracy, explainability, retraining, and the step from "runs on one client's data" to "runs for every client". You work with our Chief AI Engineer and directly with the client's asset, leasing and finance teams.
What you will work on
• Forecasting: tenant turnover, hourly footfall per location over a 12-month horizon, energy use, collections and cash flow.
• Risk scoring: tenant payment-delay risk, brand risk scores, early warnings on underperforming units.
• Lease analytics: estimated rental value (ERV) and which contracts sit below market.
• Explainability: every output ships with drivers (SHAP) and a confidence range a leasing manager can understand.
• MLOps: moving from scheduled scripts to proper experiment tracking, a model registry and per-client training, with clear accuracy reporting back to the product.
• LLM quality: the AI assistant answers questions over these numbers with 53 tools. You help design its evaluation: test sets, terminology rules, and the Langfuse feedback loop.
Beyond real estate, the same skills apply to our energy pricing and banking risk work, so you will see more than one industry.
Our stack
Area What we use
Modelling Python, pandas, scikit-learn, XGBoost, Prophet and other time-series methods
Explainability SHAP
Orchestration and serving Apache Airflow for monthly training and scoring runs, FastAPI compute service, MLflow being introduced
Data PostgreSQL and PostGIS, Excel and ERP exports from client systems
LLM Anthropic Claude via API, RAG over client data, tool calling and MCP, Langfuse for conversation and quality monitoring
Infra Azure, Docker, Jenkins, Bitbucket
How we build Jira, spec-first work with AI coding tools (Claude Code), code review on every PR
Your first six months
• First month: you know one live model inside out (data, features, metrics, failure modes) and ship one measurable improvement to it.
• Month three: you own a model family (for example tenant risk) including retraining, monitoring and the accuracy numbers clients see.
• Month six: you lead the modelling side of a new client rollout or new module, from use-case scoping to an advisory-mode pilot, and mentor a junior.
What you bring
• 5+ years building ML models that ran in production and changed real decisions, not only research or dashboards.
• An MSc or PhD in a quantitative field (statistics, computer science, economics, operations research), or equivalent depth.
• Strong time-series forecasting, plus at least one of: risk or default scoring, demand forecasting, pricing.
• Sound statistics: you know when a backtest lies, how to handle sparse or messy client data, and how to explain uncertainty to a CFO.
• Production Python: clean, tested, reviewable code an engineer can deploy without rewriting it.
• Experience putting models on a schedule (Airflow or similar) and tracking them (MLflow or similar).
• Fluent written and spoken English.
Nice to have
• Retail, shopping centre, real estate or energy data.
• LLM evaluation in production: test sets, LLM-as-judge, failure analysis from real user sessions.
• Geospatial data or footfall and sensor data.
• Multi-tenant model training: one pipeline, many clients, different data quality.
• Turkish, helpful for working with current client teams.
How we work
Every model starts in advisory mode next to the client's current process and earns trust by beating it on agreed metrics. You present results to client teams yourself, so you hear quickly whether a model helps.
Hiring process
10. 30-minute intro call.
11. 30-minute session: present your case to the CPO and an engineer.
12. 15-minute call with a founder.
What we offer
• Fully remote, with overlap on UK and Türkiye working hours.
• Models in production with real outcome data coming back every month.
• A PhD-heavy AI team to think with.

We are looking for a subject matter expert and technical editor to review and refine the technical content of our **Data Science book (Chapters 4–10)**.

The ideal candidate should have a strong understanding of Data Science and experience in technical editing or writing. The primary responsibility will be to ensure the technical accuracy, clarity, and coherence of the content while identifying any errors, gaps, or areas that require improvement.

Skills required:
- Excellent command of the English language
- Proficiency in “Data Science“
- Strong proofreading and editing skills
- Technical writing expertise
- Attention to detail
- Ability to meet deadlines

We are seeking an intermediate-level professional who can deliver high-quality work within the given timeframe. Previous experience in editing technical content is highly preferred.

Looking for somebody with Experience in SEO ( Search Engine Optimisation) Data Science knowledge.

I run both thedatascientist.com and tesseract.academy, and I can place your sponsored article on whichever of the two sites fits your content best. The moment you send me the final draft―or ask me to write it for a small additional fee―I can have it live, usually within 24 hours.

Each placement includes a do-follow backlink, permanent hosting, and full indexing so you gain lasting SEO value. Topics around data science, AI, analytics, entrepreneurship, or tech in general perform particularly well, but I’m open to related niches as long as they align with the readership.

Rates start at just $40 per post, making this an affordable way to secure a high-quality link and targeted exposure.


• https://thedatascientist.com/ – $40
• https://tesseract.academy/ – $40


Deliverables:
• Sponsored article published on thedatascientist.com OR tesseract.academy
• One do-follow backlink placed naturally in the content
• Permanent, live URL confirmation sent immediately after publication

Send over your article (800–1,200 words is ideal), preferred anchor text, and target URL, and I’ll take it from there. If you need it written, mention that in your message and I’ll add copy-creation to the package.

Skills: Copywriting, SEO, Link Building, Blog, Technical Writing, Article Writing, Content Writing, Digital Marketing, Blog Writing, Content Creation

We need a UAE-based hands-on AI Solutions Architect and Technical Lead for a UAE-based enterprise technology business, initially part-time with potential for ongoing engagement. The role includes leading customer technical discussions, evaluating technology partners, designing secure AI solutions, supporting demos and pilots, and guiding engineers through implementation. The ideal candidate has practical experience with LLMs, AI agents, RAG, enterprise integrations, and cloud or on-premises deployment.

We need a freelancer to train, evaluate, and fine-tune AI models and chatbots for our projects. The work includes improving model performance, testing responses, and refining outputs to meet project goals. You should be comfortable working with AI systems, analyzing results, and making adjustments to improve accuracy and effectiveness. This is a part-time role for someone who can support ongoing AI development and help ensure models deliver reliable, high-quality results.

We’re Sharpe Minds, a McGill team in the FIAM final challenge. We run a daily-decision, US$1bn long/short equity book on a monthly XGBoost cross-sectional return model. The model is trained on 147 firm characteristics with expanding-window validation, and the portfolio is beta-neutral and sector-ranked. Our development work shows that the phase-1 edge largely disappears under the new rules. The short-leg profits came from names below $500m, which are now unshortable. Net alpha is roughly zero on the 2021–26 development window, and costs run about 3% a year. We are hiring a quant on a short contract, ahead of the event, to: (1) rebuild the signal for the new tradable universe (≥$500m, 15% of ADV capacity limits, 15 bp half-spread, next-day execution), so the decile spread holds up in 2024–26; (2) fix the portfolio construction and turnover logic (hysteresis, multi-day execution, beta and sector neutrality) so the book is at least gross-positive against T-bill + 4%; and (3) independently audit our walk-forward protocol, including test-period tuning, look-ahead and survivorship (the 8-K corpus omits delisted firms), because the brief disqualifies undisclosed leakage. You’ll need strong cross-sectional equity factor and ML experience (Python, pandas, XGBoost), a rigorous approach to backtesting and transaction-cost modelling, and the ability to deliver tested, documented code and a short methodology memo within days. A clean, reproducible pipeline matters more to us than a high backtest number.

Purpose: Build a cloud-based Occupational Health & Safety (OHS) compliance platform (OHS SaaS Platform)
Core Objectives:
• Digitize OHS compliance for enterprises
• Provide audit scoring, incident logging, training, committee management
• Generate MIFOTRA-aligned inspection reports • Support sector-specific risk profiles (construction, corporate, logistics, manufacturing) Primary Users:
• OHS Managers
• Committee Members
• Employees
• Contractors
• External Auditors
• Senior Management

Completing is hiring an experienced analyst for a 90-day contract on a management consulting engagement with a large nationwide organization. The work combines workforce data analysis with a confidential employee survey to explain why people leave and how they experience the organization.

What you'll work on
- Turnover and retention: who leaves and from where, voluntary vs. involuntary separations, and new-hire retention.
- Cost drivers: compensation, overtime, absenteeism, vacancies, scheduling, and the cost of replacing people.
- Leadership and working conditions, and how they relate to departures.
- An employee survey: design the instrument, set confidentiality safeguards, analyze views of supervisors and senior leadership, and compare departments where anonymity allows.
- Interview and focus-group questions that line up with the survey.

Deliverables
- Executive workforce dashboard
- Turnover and retention analysis, with a turnover-cost model
- Employee survey, confidentiality protocol and results analysis
- Organizational culture findings report with quantified opportunities for improvement

What you bring
- 5+ years of applied workforce analysis for large enterprise companies.
- Experience running confidential workforce or employee assessments. Marketing or customer-satisfaction surveys alone aren't a fit.
- A degree in data science, data analytics or a related field.
- Strong Excel and statistics skills. SQL, R or Python is a plus.
- Clear writing for executives.

The engagement
- 90-day independent contract (1099), starting January 2027.
- About 10 to 15 hours a week, remote. US-based candidates only.
- Rate is open and based on experience.

To apply, send a cover letter and links or documents for case studies of similar projects you've supported.

We’re looking for AI trainers to evaluate AI responses, improve output quality, and support AI model training. You’ll review generated responses, identify issues, and help refine outputs to ensure accuracy and consistency. Strong analytical skills and attention to detail are essential. Previous AI training experience is a plus. This is a remote, freelance opportunity with competitive pay.

Required Skills
AWS, Anthropic, TypeScript, Docker, Terraform, Python, C#, OpenAI, PostgreSQL


About Role

The role is a full‑stack AI software engineer responsible for designing, shipping, and operating customer‑facing AI systems across the entire product lifecycle—from discovery and prototyping through deployment and monitoring—working directly with customers, product managers, and engineering teams with high autonomy and impact.

Responsibilities

Design, build, and operate systems in customer‑facing production environments

Translate ambiguous business and customer problems into prototypes and technical specifications

Track emerging AI capabilities and apply them where they create clear leverage

Own the full application lifecycle including discovery, experimentation, evaluation, deployment, and monitoring

Make system design decisions, manage trade‑offs, and ensure long‑term scaling and maintainability

Write legible codebases for agents and developers and drive tooling improvements across the organization

Partner with product managers to enhance product vision with AI‑native solutions

Requirements

2+ years of experience in software engineering or applied AI

Strong applied AI and software engineering fundamentals

Experience working with AI development tools in full‑stack applications

Experience designing AI‑based solutions for real workflows

Working knowledge of cloud platforms and frontier AI platforms such as Anthropic or OpenAI

Ability to span technology, product, and design thinking with high autonomy

Bias for shipping, iterating, and incorporating customer feedback over polish

Curiosity to continuously improve systems, products, and personal craft

Degree in Computer Science, Engineering, Data Science, or related field (preferred)

Experience operating AI systems in production with attention to evaluation, cost, and performance trade‑offs (preferred)

Background in high‑ambiguity environments close to customers (e.g., early‑stage startups, forward‑deployed engineering, internal product teams) (preferred)

Experience with large‑scale Python, C#, and TypeScript codebases (preferred)

Experience integrating AI solutions into existing, established products (preferred)

Senior Software Engineer – AI & Data Platforms

Location: Remote
Experience Level: Senior (6+ years)
Project Type: Part-time Contract
Language Requirement: Native-level English

About the Role

We’re seeking an experienced Senior Software Engineer specializing in AI platforms, data engineering, and machine learning infrastructure. You’ll architect and build enterprise-scale systems that power intelligent applications, focusing on production-grade AI solutions and cloud-native data platforms.

Key Responsibilities
Design and implement enterprise AI platforms supporting LLM integration, RAG architectures, and generative AI applications
Architect scalable data platforms for ingesting, processing, and serving large volumes of data (millions of records/month)
Build production ML platforms including model training pipelines, deployment automation, monitoring, and governance
Develop secure REST APIs and microservices using Python, Java, or TypeScript
Implement CI/CD automation, containerization, and infrastructure-as-code solutions
Optimize distributed data processing pipelines and improve system performance
Collaborate with data scientists, product managers, and cross-functional teams to deliver production-grade solutions
Required Skills & Experience

Core Technologies:

Languages: Python, Java, or TypeScript/JavaScript
AI/ML: Generative AI, LLMs, RAG, prompt engineering, model deployment
Data Engineering: Spark, Kafka, ETL/ELT pipelines, SQL
Cloud Platforms: AWS or Azure (or both)
DevOps & Infrastructure: Docker, Kubernetes, Terraform, CI/CD pipelines
Databases: PostgreSQL, MongoDB, Snowflake, Databricks, or similar

Communication & Experience:

Fluent, native-level English (verbal and written) – ability to communicate clearly in meetings, documentation, and code reviews
9+ years of software engineering experience
Proven track record shipping production AI/ML systems
Strong understanding of system design and scalability
Experience building cloud-native applications
Ability to work independently and communicate effectively in a remote setting

Nice-to-Have:

Experience with LLM platforms (OpenAI, Azure OpenAI, Llama, LangChain, LlamaIndex)
Vector databases and semantic search implementations
MLOps and ML platform development
Healthcare, fintech, or enterprise software experience
Kubernetes orchestration at scale
Microservices and distributed systems architecture
What We’re Looking For
Excellent communication and collaboration skills
Problem-solving mindset with attention to detail
Proactive in asking clarifying questions and providing updates
Ability to mentor junior engineers
Experience working in agile/fast-paced teams
Deliverables
Well-architected, production-ready code
Automated testing and CI/CD pipelines
Documentation and knowledge sharing
Regular progress updates and collaboration

PokerDen is hiring a data engineer to build the game's data foundation: recording every hand, tracking how players use the game, and supplying clean data to our AI work.
PokerDen is a social Texas Hold'em game: players create an account, take a seat and play with friends in real time. It uses play chips only, with no real-money betting.
The working MVP has a full Hold'em engine, real-time tables over Socket.io, and player accounts with chip balances saved in MongoDB. Hands are played out on the server, but their history is not saved yet, and there is no analytics or data pipeline. You'll build that from the start.
What you’ll do
You'll own PokerDen's data from the moment it is created to the moment someone uses it. In your first months, that means:
1. Record every hand. Design and build hand history storage: every deal, action, pot and result, linked to the players and table, in a format that is easy to query and replay.
2. Track how the game is used. Set up event tracking for sign-ups, sessions, table joins, chip top-ups and churn, and build the dashboards the team uses to decide what to build next.
3. Watch the chip economy. Report how chips enter and leave the game, and alert the team when balances move in ways that suggest a bug or cheating.
4. Feed the AI work. Give the AI engineer reliable, well-documented datasets for training poker bots, detecting unfair play and evaluating models.
5. Make it production-ready. Pipelines ship with tests, data quality checks, monitoring, backups and clear ownership, and they don't slow down live games.
6. Protect player data. Keep personal information separate and access-controlled, set retention rules, and support data export and deletion requests.
You'll work with the full-stack engineer, who owns the game server, the AI engineer, who uses the data, and directly with the project owner on priorities.

PokerDen is hiring an AI engineer to design, build and ship the game's first AI features, and to run them reliably in production.
PokerDen is a social Texas Hold'em game: players create an account, take a seat and play with friends in real time. It uses play chips only, with no real-money betting.
The working MVP has a full Hold'em engine, real-time tables over Socket.io, and player accounts with chip balances saved in MongoDB. It has no AI features yet. You'll be the person who decides how AI fits into the game and gets it into players' hands

- What you’ll do
You'll own PokerDen's AI work from prototype to production. In your first months, that means:
1. Build AI opponents. Create poker bots at several skill levels so players always find a game, even at quiet tables. Bots are always clearly labelled as bots, and they see only what a human player in their seat would see.
2. Protect fair play. Detect collusion, chip dumping, multi-accounting and players using bots or outside help, using game history and behaviour signals. Flag cases for review instead of banning automatically.
3. Moderate chat. Filter abuse, spam and personal information in lobby and table chat, with low enough delay that conversation still feels live.
4. Add a hand review assistant. After a hand ends, players can ask an LLM to explain what happened and what they might have done differently. It never gives advice during a live hand.
5. Make it production-ready. Each feature ships with evaluation sets, monitoring, cost and latency budgets, fallbacks for when a model or provider fails, and a way to roll changes back

You'll work with the full-stack engineer, who owns the game server, and directly with the project owner on what to build next.
What you bring

- 4+ years in software engineering, with 2+ years shipping machine learning or LLM features that real users rely on
- Strong Python, and enough TypeScript or JavaScript to work on our Node.js game server
- Hands-on experience building with LLM APIs: prompt design, tool use, structured outputs, and evaluating model quality
- Experience taking AI features to production: serving, monitoring, cost control, and handling model failures safely
- Solid machine learning basics: classification, anomaly detection, and choosing the right metrics for imbalanced data
- A habit of measuring: you build test sets before you tune, and you can show whether a change made things better
- Good judgement about player trust and privacy: what data to use, what to log, and when a person should make the final call
- Clear written English for async work

About the role
Appraiva is looking for a hands-on Software Engineer to help build and grow our AI-native product. You’ll join a fast-moving team tackling interesting engineering challenges across data systems, web applications, and AI, with opportunities to take ownership of engineering work across our current and upcoming product lines.

Taking ownership of our production data and backend systems will be a core part of your role, alongside occasional technical customer requests involving data, APIs, and integrations. You’ll also contribute to our web applications on AWS. The balance will shift with what we’re building: some projects will focus on data infrastructure, while others will involve substantial web development and opportunities to build new AI capabilities.

We’re looking for someone who enjoys figuring out what needs to be done, proposing practical solutions, and turning ideas into working software. If you like variety, learn quickly, and get excited about what’s becoming possible with AI, you’ll have plenty to dig into here.

What you’ll do
Own production data and backend systems
Own, optimize, and monitor the production data pipelines and backend services that power our applications on AWS.
Investigate production incidents, restore service, and safely recover failed jobs or affected data.
Identify root causes and implement lasting fixes to prevent recurring issues, while proactively improving data quality, performance, and efficiency.
Improve logging, alerts, tests, and recovery documentation so that systems are easier to operate and troubleshoot.
Build and improve our web applications
Contribute features, fixes, and improvements to our existing web application codebases on AWS.
Take on more substantial web development work as priorities evolve, defining practical technical approaches and moving changes from early exploration through release.
Use AI development tools to accelerate your work while reviewing, understanding, and validating the code you ship.
Contribute to AI engineering
Help explore and build AI capabilities across our current and upcoming product lines.
Propose and prototype AI approaches, evaluate what works, and help turn promising ideas into reliable product features.
Work through practical challenges in connecting data, application logic, and AI to build dependable user-facing capabilities.
Handle occasional technical customer requests
Handle customer-specific data requests and troubleshoot issues across databases, APIs, and integrations using queries, scripts, and targeted backend fixes.
Work with teammates and, when needed, customers to clarify requirements, validate results, and communicate the resolution clearly.
Identify recurring requests or issues that would benefit from reusable queries, automation, or system improvements.
What we’re looking for
Strong Python, SQL, and hands-on production AWS experience are required.

Experience owning production data pipelines or backend systems, including incident recovery, root-cause analysis, and lasting fixes.
Strong SQL skills and confidence working with databases, data transformations, and data quality issues.
Strong Python skills, including building and debugging production data pipelines or backend services, with the ability to navigate existing codebases and make maintainable changes.
Experience working with APIs and integrating data across systems.
Hands-on production AWS experience deploying, operating, monitoring, and troubleshooting data pipelines or backend services.
Experience with version control, testing, code review, and deployment workflows.
Curiosity about AI engineering and an appetite for learning new tools and approaches. Experience building AI features or applications is a plus.
Comfort using AI coding tools while taking responsibility for the quality and correctness of the result.
Initiative, clear communication, and sound judgment when requirements are still taking shape, with the ability to make progress independently and follow work through to completion.

We are looking for a reliable B2B Lead Generation Specialist to help identify decision-makers in US electrical distributors, builder supply companies, and electrical OEMs.

The goal is not bulk lead collection. We need accurate contact mapping of people involved in supplier selection, sourcing, category decisions, and vendor management.

Deliverables:

* Relevant contacts by company
* Job title and LinkedIn profile
* Verified business email
* Company/role start date (if available)

Ideal candidates have experience with industrial B2B research, LinkedIn sourcing, and decision-maker identification. Quality, accuracy, and attention to detail are more important than volume.

We follow a rule-based SPX 0DTE options bot that trades day (09:30–16:00 ET) and overnight (20:15–09:25 ET) with the same rules. Each day it publishes a morning report (00:00 ET) and a midday report (08:00 ET) with: pivot point, support, resistance, gravity level, expected range, remaining expected move, market bias, market state, and its own 1-hour Murrey Math levels (support pivot / stability line / equilibrium / buy and sell zones). It also shows a live screen: session direction, 1/5/15/30-min DMI, instant liquidity, walls, and a contract ladder.

We already reproduced most of it:
- side = direction of the last 15-min SPX move (+ position vs the report pivot)
- trigger = first break of the previous 1-hour candle's high/low in that direction, price holding ~45–60 s beyond it
- contract = nearest OTM SPXW with ask ≤ $4.00; stop = 50%, targets T1–T7, add after a fast T1
- we rebuilt their report (14/17 numbers match) and their screen

Our replica fires ~10 signals per night; the bot takes ~4 and skips the rest. We need the missing FILTER that decides which signals it takes, plus the few entries that are not prev-hour breaks (e.g. rebounds to a Murrey line).

You get: every signal since 5 Oct labeled taken/skipped, 1-s SPX and option quotes, our live screen fields per minute, all daily reports (theirs and ours), chart images, and the bot's entry/exit times.

Acceptance:
1) fit on 5–19 Oct only; we score once on unseen days (20–23 Oct)
2) Final acceptance is LIVE: your rule runs on our live screen data for 3 trading days (day + overnight) and must match the bot's real entries as they happen.
Pass = precision ≥ 40% and recall ≥ 50% per session, entry within 60 s. The rule must be causal and delivered as plain logic + a Python function.

Payment: fixed price in 2 milestones; the second is paid only after the live test passes.
Skills: Python, pandas, scikit-learn, intraday options trading; TradingView/Pine Script a plus.

We are looking for a reliable B2B Lead Generation Specialist to help identify decision-makers in US electrical distributors, builder supply companies, and electrical OEMs.

The goal is not bulk lead collection. We need accurate contact mapping of people involved in supplier selection, sourcing, category decisions, and vendor management.

Deliverables:

* Relevant contacts by company
* Job title and LinkedIn profile
* Verified business email
* Company/role start date (if available)

Ideal candidates have experience with industrial B2B research, LinkedIn sourcing, and decision-maker identification. Quality, accuracy, and attention to detail are more important than volume.

BIGEdge™ is built on data, models and intelligence. We have an MVP and are now moving toward GA.

We are looking for a senior Data Scientist to lead the data science behind BIGEdge and own the models, scoring methodology, data quality and intelligence layer that power the platform.

We are open to a fractional/part-time engagement with the right person. This could be an ongoing role rather than a traditional full-time position.

We are a small team, so this is a highly hands-on role. You should be equally comfortable setting direction with the founders and Product team, challenging methodology and priorities, and then getting into the weeds to inspect data, write code, test models, troubleshoot issues and improve performance yourself.

You will work closely with our Lead Engineer, who owns the application and platform layer, while you own the data, AI and intelligence layer.

What You’ll Own

MVP to GA

Assess the current MVP data and model architecture and identify what needs to change before GA

Strengthen model accuracy, validation, Data Sufficiency and monitoring

Improve onboarding, data-source reliability and production readiness

Help prioritize what needs to be completed for GA versus what can wait

Ensure the intelligence layer can scale across more companies, sources and users

Models, Scoring & Validation

Own and improve BIGEdge’s scoring, classification and intelligence models

Lead work across lever classification, risk, sentiment, Employee Commitment, Customer Advocacy, intent and AI-generated insights

Own the statistical methodology behind BIGEdge scores and outputs

Measure model performance by lever and category using appropriate measures such as precision, recall, false positives, thresholds and agreement

Build and maintain validation approaches including golden sets, held-out testing and human review

Identify weak models, edge cases and areas where performance needs to improve

Own Data Sufficiency methodology and determine when there is enough evidence to support a score or insight

Monitor model performance and lead recalibration or retraining where needed

Make sure our models and scores can be explained, tested and trusted

Data Sources & Quality

Own the strategy for how BIGEdge uses external, customer-provided and internal data

Assess source quality, coverage, bias and dependency

Understand how results change when source coverage changes or a source becomes unavailable

Help determine how different types of data should be used, compared or combined

Establish standards for data quality, relevance, recency, deduplication and entity matching

Work with Product and Legal on data provenance, vendor dependencies and appropriate use of third-party data

NLP, Machine Learning & AI

Develop and improve NLP and machine-learning models used to classify and interpret unstructured text

Refine taxonomies and classification logic across Leadership, Culture and Reputation

Evaluate and improve traditional ML and LLM-based approaches

Support RAG, retrieval and AI-agent capabilities where they improve the product

Improve model accuracy, explainability and reliability

Establish strong practices for deployment, monitoring and model lifecycle management

Data Platform & Onboarding

Improve the data architecture across ingestion, processing, storage and access

Build and maintain reliable data pipelines across multiple sources

Own the data setup and onboarding process for new companies, including company and competitor setup, source configuration, historical backfill, validation and readiness for scoring

Partner with Engineering to integrate model outputs into the product

Help ensure the platform can scale as the number of companies and data sources grows

Product & Client Intelligence

Turn raw data into structured insights that are useful in the product and in client delivery

Make sure scores, themes, trends, benchmarks and insights are supported by the underlying data

Work with Product to make data and AI outputs understandable and useful

Work with Advisory to translate data into meaningful client insights without overstating what the evidence supports

Help evolve the proprietary intelligence and methodology that differentiates BIGEdge

How You’ll Work

Operate as the primary Data Scientist in a small team

Move comfortably between senior-level strategy and hands-on technical work

Challenge methodology, recommend changes and help implement them

Take ownership from problem definition through analysis, build, validation and production

Get into the detail when something does not look right, whether that means reviewing data, debugging a pipeline, testing a model or inspecting individual classifications

Partner closely with the Lead Engineer, Product, Advisory and leadership

Explain complex technical issues clearly to non-technical stakeholders

Document models, methodology and processes so critical knowledge is not held by one person

What You Bring

7+ years of experience in data science, machine learning, AI or related roles

Strong foundation in statistical modelling and model validation

Strong Python and SQL skills

Strong experience with NLP, text classification and unstructured data

Experience evaluating multi-label classification systems, taxonomies or similar frameworks

Strong understanding of precision, recall, false positives, threshold setting and model performance

Experience taking ML or AI models from development into production

Experience with LLMs, RAG and modern AI approaches

Experience with cloud infrastructure, preferably GCP

Experience with production data pipelines and MLOps

Comfortable working with noisy, incomplete and multi-source data

Strong ability to translate technical results into business insight

Comfortable taking ownership and working independently

Nice to Have

Experience building scoring models, indices or proprietary analytical frameworks

Experience with alternative data, web-derived data or multi-source external datasets

Experience with employee, customer, reputation or people-related data

Experience with survey data and open-text analysis

Experience with causal inference, experimentation or advanced statistical methods

Experience working in an early-stage or product-led environment

Experience supporting products used by senior executives, boards or investors

What to Expect

This is an important role for BIGEdge. You will have a direct impact on the quality of the data, models and insights our clients see.

We are looking for someone who is deeply technical but practical, who cares about whether the data is actually right, and who can help us take BIGEdge from MVP to GA and beyond.

We are open to structuring this as an ongoing fractional engagement for the right person.

Job Description – Senior / Lead Machine Learning Engineer (AI/ML)

Company: Saradysol Tech Private Limited
Experience: 15+ years; exceptional candidates with equivalent expertise considered
Location: Bengaluru / Remote / Hybrid, based on business requirements
Employment Type: Full-Time

About the Role

We are looking for a hands-on Senior / Lead Machine Learning Engineer who combines strong AI/ML expertise with excellent communication, technical leadership, mentoring skills, and end-to-end ownership.

You will translate business problems into practical AI solutions, design architecture, develop production-ready systems, and guide engineers throughout delivery. The ideal candidate can work independently, make informed decisions, explain complex concepts clearly, and take responsibility for results.

Key Responsibilities

1. End-to-End Ownership and Delivery
- Own AI/ML initiatives from requirement gathering and feasibility assessment through development, deployment, monitoring, and continuous improvement.
- Translate business objectives into clear technical requirements, delivery plans, and measurable success criteria.
- Take responsibility for solution quality, technical decisions, timelines, and production performance.
- Proactively identify risks, dependencies, and blockers, and drive their resolution.
- Communicate progress transparently and escalate issues early with proposed solutions.
2. Machine Learning and Generative AI Development
- Design, develop, evaluate, and optimize machine learning and deep learning solutions.
- Apply appropriate techniques for data preparation, feature engineering, model selection, validation, and performance evaluation.
- Build applications using LLMs, retrieval-augmented generation (RAG), embeddings, vector databases, and agent workflows where appropriate.
- Select suitable approaches based on business needs, data availability, accuracy, latency, cost, and maintainability.
- Establish evaluation baselines, prevent data leakage, and document assumptions and model limitations.
- Assess emerging AI technologies and apply them where they offer practical value.
3. Production Engineering and MLOps
- Write clean, modular, maintainable, and well-tested Python code.
- Build APIs and integrate AI capabilities into enterprise applications.
- Implement reproducible pipelines, experiment tracking, model versioning, automated testing, and CI/CD.
- Deploy and operate solutions using cloud services, Docker, and appropriate MLOps tools.
- Monitor model quality, data drift, reliability, inference latency, and operating costs.
- Troubleshoot production issues and implement sustainable improvements.
- Apply appropriate security, privacy, access-control, and responsible AI practices.
4. Communication and Stakeholder Collaboration
- Explain complex AI/ML concepts clearly to technical and non-technical audiences.
- Lead requirement discussions and ask questions that clarify business needs and expectations.
- Present architecture proposals, evaluation results, technical recommendations, and delivery updates professionally.
- Communicate trade-offs, risks, uncertainty, and limitations openly.
- Write clear technical documentation, architecture explanations, and decision records.
- Collaborate effectively with software engineers, data engineers, DevOps teams, product teams, customers, and management.
5. Technical Leadership, Mentoring, and Teaching
- Provide hands-on technical direction through architecture discussions, code reviews, and problem-solving sessions.
- Mentor junior and mid-level engineers with practical guidance and constructive feedback.
- Explain both how to implement solutions and why specific approaches are appropriate.
- Conduct technical training, knowledge-sharing sessions, and architecture walkthroughs.
- Establish coding standards, engineering practices, and production-readiness expectations.
- Lead through expertise, evidence, and collaboration, including without formal people-management responsibility.
Required Technical Skills
- Programming: Strong Python, SQL, debugging, data structures, and software engineering fundamentals.
- Machine Learning: Supervised and unsupervised learning, feature engineering, model selection, statistical foundations, and evaluation.
- Deep Learning: Hands-on experience with PyTorch or TensorFlow.
- Generative AI: LLM applications, RAG, embeddings, vector search, prompt design, and output evaluation.
- Frameworks: scikit-learn and relevant AI libraries.
- Data Pipelines: Data preprocessing, validation, feature pipelines, and reproducible workflows.
- MLOps: Experiment tracking, model versioning, deployment, and monitoring using MLflow or equivalent.
- Deployment: REST APIs, FastAPI or equivalent, Docker, Git, automated testing, and CI/CD.
- Cloud: Hands-on experience with at least one of AWS, Azure, or GCP.
- System Design: Scalable architecture, observability, reliability, security, and performance optimization.
Essential Professional Qualities
- Excellent communication: Speaks and writes clearly, listens actively, and explains complex topics simply.
- Strong ownership: Drives work to completion and follows through without constant follow-up.
- Technical leadership: Guides engineers, makes sound decisions, and leads by example.
- Teaching mindset: Explains concepts patiently and helps others develop independent problem-solving skills.
- Structured problem-solving: Investigates root causes and evaluates alternatives using evidence.
- Proactive approach: Anticipates challenges and acts before they affect delivery.
- Accountability: Honors commitments and communicates early when priorities or timelines change.
- Business understanding: Connects technical decisions to measurable business outcomes.
- Professional maturity: Accepts feedback and handles disagreements respectfully.
Qualifications and Experience
- Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, Data Science, Engineering, or equivalent practical experience.
- Demonstrated experience developing, deploying, and supporting production AI/ML systems.
- End-to-end ownership of at least one substantial AI/ML initiative, with clear evidence of individual contribution.
- Experience making architecture decisions and explaining technical and business trade-offs.
- Evidence of mentoring engineers, leading technical initiatives, or conducting technical training.
- Ability to work independently while collaborating effectively across teams.